You are here

Data Intensive Sciences

Data Intensive Sciences focuses on understanding and exploiting the fundamental aspects of large-scale multidimensional data analytics. Experiments, observations, and numerical simulations are on the verge of generating petabytes of diverse data at ever increasing rates. These sources of data are distributed over disparate locations on a heterogeneous collection of platforms and pose a challenge in providing real-time analytics that support U.S. military operations.

Time-Sensitive and Distributed Data Analytics

Time-sensitive analytics consist of 3 time scales: (1) real time - processing data as it arrives, typically on the order of milliseconds; (2) time-critical – processing data in a finite but short time window, fast enough to support the event as it happens, typically on the order of seconds; and (3) on-demand – processing data at the request of a commander, decision maker, manager, or planner, typically from minutes to hours.

Commercial and open source stream processing software are being examined to enhance the value and utility of real-time data. High fidelity network emulation tools aid in understanding the impact on performance of data movement, distribution of resources, network protocol mix, effects of terrain and mobility, and to more faithfully predict rare event occurrences. Improved numerical linear algebra algorithms and visualization are required for dynamically changing graphs.

Adaptive Computing for Artificial Intelligence & Machine Learning

Novel heterogeneous computing resources, such as neuromorphic and other data-flow architectures, can be dynamically allocated, adapted, and accessed for demanding artificial intelligence & machine learning processing. Dynamically adaptive algorithms and software for on-line learning and reasoning using extremely small weight power and time (SWAPT) computing resources under constraints of limited communications.We seek collaborations in the following areas:

Techniques for adaptive allocation of computing resources to tasks in an environment with rapidly changing connectivity and availability of assets

Real-time adaptation of algorithms for available hardware, tasks, and time available

Methods for optimized real-time reconfiguration of hardware based on properties of the tasks and available software

Characterization of capabilities and constraints of novel computing architectures (e.g., neuromorphic) for learning and reasoning processes